{
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    {
      "cell_type": "code",
      "execution_count": null,
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        "%matplotlib inline"
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      "source": [
        "\n=========================================\nSGD: Maximum margin separating hyperplane\n=========================================\n\nPlot the maximum margin separating hyperplane within a two-class\nseparable dataset using a linear Support Vector Machines classifier\ntrained using SGD.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "source": [
        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import SGDClassifier\nfrom sklearn.datasets.samples_generator import make_blobs\n\n# we create 50 separable points\nX, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)\n\n# fit the model\nclf = SGDClassifier(loss=\"hinge\", alpha=0.01, max_iter=200, fit_intercept=True)\nclf.fit(X, Y)\n\n# plot the line, the points, and the nearest vectors to the plane\nxx = np.linspace(-1, 5, 10)\nyy = np.linspace(-1, 5, 10)\n\nX1, X2 = np.meshgrid(xx, yy)\nZ = np.empty(X1.shape)\nfor (i, j), val in np.ndenumerate(X1):\n    x1 = val\n    x2 = X2[i, j]\n    p = clf.decision_function([[x1, x2]])\n    Z[i, j] = p[0]\nlevels = [-1.0, 0.0, 1.0]\nlinestyles = ['dashed', 'solid', 'dashed']\ncolors = 'k'\nplt.contour(X1, X2, Z, levels, colors=colors, linestyles=linestyles)\nplt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired,\n            edgecolor='black', s=20)\n\nplt.axis('tight')\nplt.show()"
      ]
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